Ubiquitous blood pressure (BP) monitoring is needed to improve hypertension detection and control and is becoming feasible due to recent technological advances such as in wearable sensing. Pulse transit time (PTT) represents a well-known potential approach for ubiquitous BP monitoring. The goal of this review is to facilitate the achievement of reliable ubiquitous BP monitoring via PTT. We explain the conventional BP measurement methods and their limitations; present models to summarize the theory of the PTT-BP relationship; outline the approach while pinpointing the key challenges; overview the previous work toward putting the theory to practice; make suggestions for best practice and future research; and discuss realistic expectations for the approach.

/pdf/toward-ubiquitous-blood-pressure-monitoring-via-pulse-3274861uea.pdf

Toward Ubiquitous Blood Pressure Monitoring via Pulse Transit Time: Theory and Practice.

https://www.internationaljournalofcardiology.com/article/S0167-5273(12)00326-9/pdf

How accurate is pulse rate variability as an estimate of heart rate variability?: A review on studies comparing photoplethysmographic technology with an electrocardiogram

The work presented in this paper aims at assessing human emotions using peripheral as well as electroencephalographic (EEG) physiological signals on short-time periods Three specific areas of the valence-arousal emotional space are defined, corresponding to negatively excited, positively excited, and calm-neutral states An acquisition protocol based on the recall of past emotional life episodes has been designed to acquire data from both peripheral and EEG signals Pattern classification is used to distinguish between the three areas of the valence-arousal space The performance of several classifiers has been evaluated on 10 participants and different feature sets: peripheral features, EEG time-frequency features, EEG pairwise mutual information (MI) features Comparison of results obtained using either peripheral or EEG signals confirms the interest of using EEGs to assess valence and arousal in emotion recall conditions The obtained accuracy for the three emotional classes is 63% using EEG time-frequency features, which is better than the results obtained from previous studies using EEG and similar classes Fusion of the different feature sets at the decision level using a summation rule also showed to improve accuracy to 70% Furthermore, the rejection of non-confident samples finally led to a classification accuracy of 80% for the three classes

/pdf/short-term-emotion-assessment-in-a-recall-paradigm-1zh6el5f7p.pdf

Short-term emotion assessment in a recall paradigm

We present a new method for automatic detection of peaks in noisy periodic and quasi-periodic signals. The new method, called automatic multiscale-based peak detection (AMPD), is based on the calculation and analysis of the local maxima scalogram, a matrix comprising the scale-dependent occurrences of local maxima. The usefulness of the proposed method is shown by applying the AMPD algorithm to simulated and real-world signals.

https://www.zora.uzh.ch/id/eprint/70606/1/Scholkmann_Algorithm_for_automatic_peak_detection_NeoUSZ.pdf

An Efficient Algorithm for Automatic Peak Detection in Noisy Periodic and Quasi-Periodic Signals

https://www.heartrhythmjournal.com/article/S1547-5271(12)01435-X/pdf

A novel application for the detection of an irregular pulse using an iPhone 4S in patients with atrial fibrillation

Beat detection algorithms have many clinical applications including pulse oximetry, cardiac arrhythmia detection, and cardiac output monitoring. Most of these algorithms have been developed by medical device companies and are proprietary. Thus, researchers who wish to investigate pulse contour analysis must rely on manual annotations or develop their own algorithms. We designed an automatic detection algorithm for pressure signals that locates the first peak following each heart beat. This is called the percussion peak in intracranial pressure (ICP) signals and the systolic peak in arterial blood pressure (ABP) and pulse oximetry (SpO/sub 2/) signals. The algorithm incorporates a filter bank with variable cutoff frequencies, spectral estimates of the heart rate, rank-order nonlinear filters, and decision logic. We prospectively measured the performance of the algorithm compared to expert annotations of ICP, ABP, and SpO/sub 2/ signals acquired from pediatric intensive care unit patients. The algorithm achieved a sensitivity of 99.36% and positive predictivity of 98.43% on a dataset consisting of 42,539 beats.

/pdf/an-automatic-beat-detection-algorithm-for-pressure-signals-549g2ed5wb.pdf

An automatic beat detection algorithm for pressure signals

The design of instrumentation used to measure the bioimpedance of skin or tissue is presented. An inexpensive, component level approach, appropriate for use by researchers rather that commercial applications, is emphasized. The design and implementation process is thoroughly explained and design tradeoffs are discussed with relation to various applications. A validation of the implementation in hardware is presented and an example application to skin impedance topography is considered.

Variable frequency bioimpedance instrumentation

We describe a new resource for developers of QRS detection algorithms that provides a standardized assessment of algorithm performance that is blinded, objective, independent, and reproducible. This overcomes the problems of using a single data set for both development and assessment that leads to favorably biased estimates of performance.

A new resource for independent and blinded assessment of QRS detection algorithms

The physiological responses to needle stimulation of an acupuncture point and a nearby control point were compared in six healthy participants The electrocardiogram (ECG), respiration, and electrodermal response (EDR) were measured along with the times of needle insertion, interim needle stimulation and needle removal In addition to the aforementioned, any relevant events such as movement of the subject, unexpected noise, etc were annotated

D. Tsunami

Papers

An automatic beat detection algorithm for pressure signals

Variable frequency bioimpedance instrumentation

A new resource for independent and blinded assessment of QRS detection algorithms

Physiologic Responses to Acupuncture Point Stimulation: A Pilot Study to Evaluate Methods and Instrumentation